Seminars

Mansi Chhajed examines how the move from home sleep patterns to disrupted hospital sleep relates to cognitive outcomes after surgery. The session combines longitudinal sleep measurements with clinical assessments of postoperative delirium to identify baseline markers of vulnerability and consider practical ways to improve recovery. It connects sleep architecture, biological and environmental influences, and preoperative risk with proactive, sleep-informed patient care. Chhajed is a Clinical Research Coordinator at Massachusetts General Hospital. Online via Zoom Webinar. Tuesday 29 September 2026 at 14:00 ET / 13:00 CT (America/New_York; EDT, UTC−4). Organized by the Sleep Research Society — Virtual Seminar Series. Follow Register Today on the organizer's page to the event-specific Zoom form, which requests name and email. The form is publicly accessible; advance registration supplies webinar access.

Vanessa Siffredi explores why the corpus callosum is central to typical brain development, yet some children born without it or with callosal abnormalities develop comparably to their peers. The talk connects callosal structure and communication between hemispheres with cognition, attention, executive function, memory, language and socio-emotional development. Structural and functional neuroimaging studies of typical development and callosal dysgenesis examine how developing brains compensate for alterations in this major white-matter pathway. Siffredi is affiliated with CHUV and UNIL; Carole Guedj chairs the seminar. Hybrid: Auditoire de pédiatrie Fred Bamatter, HUG, Hôpital des Enfants, Geneva; online via Zoom. Tuesday 29 September 2026, 09:30–10:30 CEST (Europe/Zurich; UTC+2), followed by CIBM news and networking until 11:00. Organized by CIBM Center for Biomedical Imaging — Breakfast and Science. In-person attendance is free without registration. Remote attendees must register through the Zoom link on the event page and select the 29 September occurrence.

Irene Rombel discusses how artificial intelligence and machine learning can address bottlenecks, quality issues, and production costs in cell, gene, and RNA therapy manufacturing. The seminar focuses on real-world process-development applications that improve reproducibility, speed, and cost effectiveness.

Merav Ahissar examines how expertise emerges through learning the statistics of task-relevant stimuli. Slow learning builds familiarity with stable regularities, such as syllable patterns in language, while fast adaptation supports flexible responses to changing situations, including surprising social interactions. The talk considers atypical learning dynamics in dyslexia and autism and how these may explain difficulties in linguistic and social skill acquisition, respectively. The discussion draws on Gertsovski and Ahissar, Trends in Cognitive Sciences, 2026. Speaker: Merav Ahissar, Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem. Organized by Cornell Cognitive Science. Friday 25 September 2026, 15:00–16:30 America/New_York (EDT). Attend online through the public Join via Zoom link on the official event page; the link opens Zoom’s app and browser join options. Cornell advertises the source event as hybrid, but this listing covers the verified online attendance route.

Mark Harnett presents work on how synaptic organization, nonlinear dendritic processing, and neuronal activity patterns interact to support computation, flexibility, and learning in the adult mammalian neocortex. The Brain and Cognitive Sciences colloquium is followed by a reception.

Florian Marquardt surveys approaches to reducing the energy and computational cost of neural networks by implementing their functions in physical devices. The central question is how experiments can efficiently determine updates to adjustable parameters, allowing the hardware itself to learn. Examples include learning through time reversal, obtaining nonlinear computation from linear optical scattering, and training analog quantum simulators as neuromorphic systems. The seminar is available in person and through the registration link on the organizer’s event page.

Qihao Ye examines practical, affordable ways for individual mathematicians to use AI in literature searches, proof development and verification. The talk identifies seven recurring failure modes and introduces the open-source QED multi-agent system designed to address them. A case study on constructing Carleman weights for wave equations on the half-line shows how automated search can be combined with a researcher’s mathematical insight and rigorous checking. The organizer links both the implementation and the accompanying research paper. Online via Rutgers DIMACS Zoom. Wednesday 23 September 2026, 10:00–11:00 EDT (America/New_York; UTC−4). Use the Zoom link and passcode on the official event page. Organized by the DIMACS AI and Mathematics Seminar. This listing advertises the verified remote attendance option.

ISTA Lecture by Klaus Robert Müller (TU Berlin & Korea University, Seoul) on how machine learning and AI enable scientific research, particularly in medicine and chemistry, and on explainability techniques for extracting understanding from machine learning models.

Bryce Mander presents research on early sleep biomarkers associated with Alzheimer's disease pathophysiology and memory impairment. The William C. Dement Seminar connects sleep and circadian neuroscience with neurodegeneration, cognition and clinically relevant approaches to detecting disease-related change.

Rani Borbara presents brain-wide recordings from more than 150 freely moving C. elegans to examine how anatomical connectivity becomes functional organization during behavior. Cross-animal models map cell-class conditional dependencies and test how synapses, gap junctions, neuromodulation and shared brain states shape circuit dynamics.

Stanford Neurosciences Seminar Series talk by Emery Brown (Massachusetts Institute of Technology, Neuroscience Statistics Research Lab) on deciphering the dynamics of the unconscious brain under general anesthesia, hosted by the Wu Tsai Neurosciences Institute.

Daphna Shohamy and Ashok Litwin-Kumar bridge experimental and computational neuroscience to explain how the brain assigns significance to memories, how dopamine shapes memory-guided decisions, and how bodily signals influence learning. Isabel Low moderates a public discussion and question session.

Omar M. Yaghi examines how reticular chemistry changes the scale and diversity of materials that chemists can design. By joining molecular building blocks into extended structures, researchers have developed large families of metal–organic frameworks and covalent organic frameworks with tunable internal spaces and functions. The lecture asks how artificial intelligence and robotics could accelerate the preparation and exploration of these materials, moving chemical discovery from scarcity toward abundance. Yaghi connects this approach to carbon capture and the collection of water from air, building on design principles for nanoporous frameworks and their applications in gas storage and separation. The discussion extends the metal–organic framework research recognised by the 2025 Nobel Prize in Chemistry into current methods for discovery and environmental applications.

Tal Linzen, an associate professor of linguistics and data science at New York University and a research scientist at Google, will speak in MIT's Siegel Family Quest for Intelligence seminar series. His work combines behavioral experiments and computational methods to study language learning and comprehension, alongside large-language-model post-training, evaluation, and interpretability.

Yuko Munakata presents evidence challenging capacity-only accounts of self-control and explains how experience, effort, and expected payoff shape executive-function engagement and later outcomes.

Zohar Ringel of the Hebrew University of Jerusalem presents a renormalisation-group approach to deep learning based on neural-network field theories. The seminar examines scaling laws, the removal of non-learnable field modes, benign overfitting and open questions about feature learning. The confirmed Quantum Matter seminar takes place in the Bob Room at Perimeter Institute on 8 September 2026, from 15:30 to 17:00 Toronto time.

Nick Turk-Browne presents research using infant functional MRI to investigate how the hippocampus supports learning and memory early in life. The seminar connects neural development, memory formation and infantile amnesia through new methods for studying awake infants.

Michelle M. Li examines how computational analyses can preserve the biological context of a proposed treatment, including cell type, disease state, genetic background and patient characteristics. She introduces Medea, an AI system that combines biological software, predictive models and literature retrieval while checking intermediate steps and reconciling evidence. The seminar presents evaluations involving cell-specific target selection, cancer-cell synthetic lethality and immunotherapy response. A separate yeast experiment tests predictions against previously unpublished measurements of gene-pair interactions under DNA-damaging treatments. The research addresses whether an agent can transfer useful evidence between contexts while recognizing when that transfer is unsupported. Reported comparisons cover predictive performance, computational failures and the ability to abstain. This recording retains the original seminar date.

Klemens Rottner (Helmholtz Centre for Infection Research, Braunschweig) presents evidence that profilin acts as a master regulator of actin filament dynamics essential for cell migration and growth, controlling both formin- and Arp2/3 complex-dependent actin assembly pathways.

Large recurrent networks are important models in several fields, including neuroscience, machine learning, physics, and applied mathematics. Yet their dynamics are difficult to study directly, because high-dimensional nonlinear systems can exhibit rich behavior that is hard to summarize in terms of individual trajectories. In this talk, I will discuss an approach that seeks to understand such dynamics through the structure of the network’s equilibria. I will focus on a random balanced network of threshold-linear units that undergoes a transition from a single stable equilibrium to extensive chaos as the disorder strength crosses a critical value. Using a combination of Kac–Rice theory, replica calculations, numerical root-finding, and dynamical mean-field theory, we show that the chaotic regime contains an exponentially large number of equilibria. These equilibria are all saddles, but with only a fractionally small number of unstable directions. Surprisingly, despite the completely random connectivity, the equilibria are not scattered randomly through phase space. Instead, they are strongly correlated and confined to a comparatively small region. The chaotic attractor lies within this same region, suggesting a direct geometric link between the organization of unstable equilibria and the collective structure of the dynamics. This picture helps explain why networks with extensive chaos can nevertheless display dynamics dominated by a relatively small number of collective modes. More broadly, the results suggest that the geometry of equilibria provides a useful complementary perspective to dynamical mean-field theory for understanding high-dimensional neural dynamics. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-05-27. Recording duration: 00:46:40.